1. Olivier Le Fèvre, Laboratoire d’Astrophysique de Marseille 1

2. INTRODUCTION 2

3. What is “Observational Cosmology” ? Observational Cosmology is the study of the structure, the evolution and the origin of the universe through observation using instruments such as telescopes Accurate facts, measurements and their errors No place for speculation ! 3

4. What are “deep surveys” ? Deep galaxy surveys are observations of a part of the sky, assembling representative samples of galaxies from well defined selection criteria Two types of complementary surveys:  Deep photometric surveys  Deep spectroscopic redshift surveys Surveys rely on large number statistics 4

5. Surveys = polls  Ask the opinion of 1 person: always wrong  Ask 10 persons: strong biases  Ask 100 persons: some biases  Ask 1000 persons: average is probably close to truth  …  Votes from the whole population make the truth 5

6. Ban the bad habits !!  Astrophysics has a bad habit: generalize from a single observation  The goal is that you’ll leave these lectures with a critical eye on observations presented in the literature 6

7. Plan of these lectures 1. Surveys: observables 2. Surveys: methods and observations 3. The Universe on large scales 4. The mass assembly and global star formation history 5. The most distant galaxies 6. Future Surveys 7

8. LECTURE #1 8

9. Why measure the Universe ?  Science knows everything !  We know the cosmological model ! So why bother ?? 9

10. Cosmology is constantly evolving… Greeks Dogons Copernicus Modern: Big Bang 10 ? Tomorrow

11. Cosmological model  Based on General Relativity  A theoretical description  Validated by some key observables  Expansion of the universe  Temperature of the microwave background  Cosmic abundance of elements 11

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13. “Accurate” cosmological parameters show our ignorance ! Dark Energy: 68.3% Dark Matter: 26.8% Ordinary Matter: 4.9% What is Dark Matter ? What is Dark Energy ? Need more observations ! 13

14. Models and Simulations  Standard  CDM in a computer  Dark matter simulations  Add physical prescriptions on top of DM  Semi-analytical models  Hydro simulations  … 14

15. Simulations produce FAKE universes ! Models need to implement ever increasing complexity Models are very useful to understand main physical processes and interplay MILLENIUM II simulation Cosmic Time 15

16. Different models = Different appearance of the universe at different cosmic times Cosmic Time Today BigBang Different models NEED Observations ! 16

17. Tracing evolution  Comparing the properties of galaxies at different epochs along cosmic time allows to derive evolution  Caveat: we cannot follow the same galaxies, hence we have to infer who is the progenitor of whom 17

18. What do we want to measure ? Cosmological parameters Galaxy Evolution scenario Statistical measurements Indirect measurements Direct measurements At different redshifts: evolution 18

19. 19 Deep galaxy surveys Distribution in LSS N(z) Galaxy density field Specifc populations Oldest Galaxies QSO / AGN Strongly starforming gal. Luminosity / SFR / Mass evolution Luminosity Function Track evolution versus Environment, Luminosity, galaxy Type,… Correlation function Luminosity Density SFR Mass function Clusters / groups Need Observations ! Cosmological parameters

20. The main tracer of the universe: Galaxies  Galaxies are (biased) tracers of the dark matter distribution  The bias can be modeled (?)  Observe galaxies and you’ll know (almost) all about the universe  Formation and evolution of galaxies  Dark matter content in galaxies and clusters  Cosmology from their distribution 20

21. Observables in deep surveys: I. Direct measurements a. Positions in space: 3D + time b. Apparent magnitudes and flux c. Sizes, morphology 21

22. Direct survey measurements: I.a. Positions in space  Measure the positions on the plane of the sky  Deep images  Measure the distances, using the redshift and a cosmological model  Redshift measurement  Redshift space vs. Real space See also the ‘cosmological distance ladder’ 22

23. Photometry from deep images SExtractor Does all what you need: astrometry, magnitudes, basic shapes See: Bertin and Arnouts, 1996, A&AS, 117, 393 and ‘SExtractor for dummies’ (Beware of the ‘black box’ syndrom) 23

24. Measuring image positions/astrometry  Use first moments of light distribution  Deblending crucial, the fainter the objects are 24

25. The Redshift  The shift in observed vs. emitted wavelength is a consequence of motion  Blueshift when moving towards the observer  Redshift when moving away from the observer  In an expanding Universe objects are moving away from each other: Redshift  Redshift is distance: v=Hd  Looking to a galaxy in rotation: velocity field with blue/red shift 25

26. Measuring photometric redshifts  Photo-z is a redshift derived from photometric data  Use the SED (Spectral Energy Distribution)  Correlate against a set of templates  Same process gives *-mass, SFR, age, etc.  Accuracy  z~3-5%  Probability distribution function  Pb of catastrophic redshifts 26

27. Measure spectroscopic redshifts Identify observed spectral features to rest-frame known features  Identify emission / absorption features  Take continuum into account Cross-correlation to galaxy templates (Tonry & Davis, 1979, AJ, 84, 1511) 27

28. Rest-frame spectrum EZ engine: Garilli et al., 2010, PASP, 122, 827 28

29. Comparing photo-z and spec-z Photo-zSpec-z  Accuracy dz~0.05(1+z)  Trained on Spec-z  Catastrophic failures: a few %  All objects detected in photometry  1 magnitude deeper than spec-z  Accuracy dz~0.001  Accurate 3D mapping  Incompletness ~10-15%  Evaluated with photo-z  30-70% of the objects seen in photometry Complementary ! 29

30. Excellent photo-z, calibrated on spec-z, Ilbert+ 13 30

31. Distances and Peculiar velocities  Galaxies have a velocity component separate from the Hubble flow v pec =v obs -H 0 d  Particularly visible in clusters because of high velocity dispersion  Finger of God effect  Distances derived from redshift measurements need to be corrected for this 31

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33. I.b. Apparent magnitudes and flux  Once objects are identified, get the total observed flux on an image  Sum the number of photons on detector  Calibrated using reference sources  Apparent magnitude m=-2.5log(Flux)+C  SExtractor  In a spectrum, get the flux in a spectral line  Sum all the photons in a line  Compute equivalent width 33

34. I.c. Sizes  Apparent sizes in arcsec, arcmin, deg  Galaxies z>0.5: arcsec- scale  Clusters of galaxies z>0.5: arcmin-scale 65”=1.08’ 5” 34

35. I.c. Morphology  Morphology of extra- galactic objects  Galaxies  Clusters/groups  Galaxies  Parametric  Non-parametric 35

36. Parametric fit to morphology See CAS (Concentration-Asymetry-Clumpiness) non-parametric classification 36

37. Observables in deep surveys: II. Indirect measurements a. Relative velocities, velocity fields, local density b. Physical sizes c. Absolute luminosities and flux d. Stellar masses, star formation rate, age, metallicity, dust,… e. Look-back time 37

38. II.a. Relative velocities, velocity fields 38

39. II.a. Local density  Density excess over mean  Environment- dependent properties 39

40. II.b. Physical sizes  Transform observed angular size to physical dimension at the source : via the cosmological model  Use angular diameter distance  See cosmology calculators: http://www.bo.astro.it/~cappi/cosmo tools  Examples: @z=1 1deg=29Mpc @z=5 1deg=23Mpc Angular scale kpc/” Redshift For  CDM 40

41. II.c. Absolute luminosities  Transform apparent to absolute magnitude Apparent in band R Absolute in band Q Distance modulus K correction 41

42. II.d. Stellar mass, star formation rate, age, dust,…  Stellar populations add up to produce a galaxy luminosity and colors  Stellar population synthesis models aim at reproducing the observed stellar light from galaxies  See Bruzual and Charlot, 2003, MNRAS, 344, 1000  Includes changes with age, with metallicity  Extinction law from dust  Difficulties with degeneracy  Age vs. Metallicity  IMF and SFR laws Synthetic spectra vs. Age (at fixed metallicity) 42

43. II.d. Spectral energy distribution fit by models 43 Photometry: over a broad wavelength range: - Tracer of stellar populations - Measurement of *-mass (red SED) - Measurement of star formation (blue SED) - Extinction - Age (of last burst of SF) Photometric measurements SED fit with stellar population template

44. II.e Look-back time  The redshift – distance relation is also a distance-cosmic time relation  Look-back time: the time it takes the light to come from an object at redshift z 44

45. Observables in deep surveys: III. Statistical measurements a. Counts N(m), N(z) b. Luminosity Functions, Luminosity Density and Star Formation Rate c. Mass Functions, Mass Density d. Correlation functions, HOD 45

46. 46 III.a Counts N(m) Count galaxies as a function of magnitude Depends on the band/wavelength History: “blue galaxy counts excess”

47. III.b Luminosity function  Luminosity Function: counts of galaxies per luminosity, per unit volume  Parametrized as a Schechter function   * = characteristic density  L*= characteristic luminosity   = faint end slope 47

48. III.b Luminosity density, SFRD  Luminosity Density: mean luminosity per unit volume  Integrate LF  SFRD: use prescriptions to transform flux into star formation  UV  IR  H   … 48

49. III.c Mass Function and density  Mass Function: counts of galaxies per stellar mass, per unit volume  Stellar mass density: integrate Mass Function 49

50. III.d Correlation Function  Excess probability over random that a galaxy in dV2 will be found at a distance r 12 from a galaxy in dV1  Contains cosmological information  Small scales: redshift space distortions  Large scales: Baryon acoustic oscillations  Halo occupation  Power spectrum P(k): Fourier Transform of Correlation function  In practice (G: galaxy sample, R: random sample):  Angular CF: w(  )  2D:  (r p,  )  Projected: rprp  50

51. From Sylvain de la Torre 51

52. III.d Correlation Function: Redshift Space Distortions  Deviations from Hubble flow produce flattening of CF on large scales along line of sight  This is linked to the growth rate of structures 52

53. III.d Correlation Function: BAO  Baryon acoustic oscillations produced when photons decoupled from matter at recombination  Leaves a signature in the CF  Peak at ~100 h -1 Mpc 53

54. Observables in deep surveys: IV. Cosmological parameters a. Hubble constant, age of the universe b. Density parameters a.  b.  m c.  b c. Equation of state d. … 54